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Primary research
Identification of key biomarkers associated with cell adhesion in multiple myeloma by integrated bioinformatics analysis
Aili He
Xian Jiaotong University
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2521-3806
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at Cancer Cell International.
Background: Multiple Myeloma (MM) is a hematologic malignant disease whose underlying molecular mechanism has not yet fully understood. Generally, cell adhesion plays an important role in MM progression. In our work, we intended to identify key genes involved in cell adhesion in MM.
Methods: First, we identified differentially expressed genes (DEGs) from the mRNA expression profiles of GSE6477 dataset using GEO2R with cut-off criterion of p < 0.05 and [logFC] ≥ 1. Then, GO and KEGG analysis were performed to explore the main function of DEGs. Moreover, we screened hub genes from the protein-protein interaction (PPI) network analysis and evaluated their prognostic and diagnostic values by the PrognoScan database and ROC curves. Additionally, a comprehensive analysis including clinical correlation analysis, GSEA and transcription factor (TF) prediction, pan-cancer analysis of candidate genes was performed using both clinical data and mRNA expression data.
Results: First of all, 1383 DEGs were identified. Functional and pathway enrichment analysis suggested that many DEGs were enriched in cell adhesion. 180 overlapped genes were screened out between the DEGs and genes in GO terms of cell adhesion. Furthermore, 12 genes were identified as hub genes based on a PPI network analysis. ROC curve analysis demonstrated that ITGAM, ITGB2, ITGA5, ITGB5, CDH1, IL4, ITGA9, and LAMB1 were valuable biomarkers for the diagnosis of MM. Further study demonstrated that ITGA9 and LAMB1 revealed prognostic values and clinical correlation in MM patients. GSEA and transcription factor (TF) prediction suggested that MYC may bind to ITGA9 and repress its expression and HIF-1 may bind to LAMB1 to promote its expression in MM. Additionally, pan-cancer analysis showed abnormal expression and clinical outcome associations of LAMB1 and ITGA9 in multiple cancers.
Conclusion: In conclusion, ITGA9 and LAMB1 were identified as potent biomarkers associated with cell adhesion in MM.
Keywords
bioinformatics analysis, multiple myeloma, biomarker
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CURRENT STATUS: Published
Version 2
Posted 04 Jun, 2020
Published
On 22 Jun, 2020
No community comments so far
Editorial decision: Accept
On 15 Jun, 2020
Reviewer #2 agreed
On 13 Jun, 2020
Review #2 received
Received 13 Jun, 2020
Reviewer #1 agreed
On 02 Jun, 2020
Review #1 received
Received 02 Jun, 2020
Reviewers invited
Invitations sent on 01 Jun, 2020
Editor assigned
On 28 May, 2020
Submission checks complete
On 27 May, 2020
Editor invited
On 27 May, 2020
No comments provided
Review #1 received
Received 02 May, 2020
Editorial decision: Minor revision
On 02 May, 2020
Review #2 received
Received 25 Apr, 2020
Reviewer #2 agreed
On 19 Apr, 2020
Reviewer #1 agreed
On 14 Apr, 2020
Reviewers invited
Invitations sent on 11 Apr, 2020
Editor assigned
On 06 Apr, 2020
Editor invited
On 05 Apr, 2020
Submission checks complete
On 28 Mar, 2020
First submitted
On 27 Mar, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
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This preprint is under consideration at Cancer Cell International. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Primary research
Identification of key biomarkers associated with cell adhesion in multiple myeloma by integrated bioinformatics analysis
Aili He
Xian Jiaotong University
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2521-3806
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 2
Posted 04 Jun, 2020
Published
On 22 Jun, 2020
No community comments so far
Editorial decision: Accept
On 15 Jun, 2020
Reviewer #2 agreed
On 13 Jun, 2020
Review #2 received
Received 13 Jun, 2020
Reviewer #1 agreed
On 02 Jun, 2020
Review #1 received
Received 02 Jun, 2020
Reviewers invited
Invitations sent on 01 Jun, 2020
Editor assigned
On 28 May, 2020
Submission checks complete
On 27 May, 2020
Editor invited
On 27 May, 2020
No comments provided
Review #1 received
Received 02 May, 2020
Editorial decision: Minor revision
On 02 May, 2020
Review #2 received
Received 25 Apr, 2020
Reviewer #2 agreed
On 19 Apr, 2020
Reviewer #1 agreed
On 14 Apr, 2020
Reviewers invited
Invitations sent on 11 Apr, 2020
Editor assigned
On 06 Apr, 2020
Editor invited
On 05 Apr, 2020
Submission checks complete
On 28 Mar, 2020
First submitted
On 27 Mar, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at Cancer Cell International.
Background: Multiple Myeloma (MM) is a hematologic malignant disease whose underlying molecular mechanism has not yet fully understood. Generally, cell adhesion plays an important role in MM progression. In our work, we intended to identify key genes involved in cell adhesion in MM.
Methods: First, we identified differentially expressed genes (DEGs) from the mRNA expression profiles of GSE6477 dataset using GEO2R with cut-off criterion of p < 0.05 and [logFC] ≥ 1. Then, GO and KEGG analysis were performed to explore the main function of DEGs. Moreover, we screened hub genes from the protein-protein interaction (PPI) network analysis and evaluated their prognostic and diagnostic values by the PrognoScan database and ROC curves. Additionally, a comprehensive analysis including clinical correlation analysis, GSEA and transcription factor (TF) prediction, pan-cancer analysis of candidate genes was performed using both clinical data and mRNA expression data.
Results: First of all, 1383 DEGs were identified. Functional and pathway enrichment analysis suggested that many DEGs were enriched in cell adhesion. 180 overlapped genes were screened out between the DEGs and genes in GO terms of cell adhesion. Furthermore, 12 genes were identified as hub genes based on a PPI network analysis. ROC curve analysis demonstrated that ITGAM, ITGB2, ITGA5, ITGB5, CDH1, IL4, ITGA9, and LAMB1 were valuable biomarkers for the diagnosis of MM. Further study demonstrated that ITGA9 and LAMB1 revealed prognostic values and clinical correlation in MM patients. GSEA and transcription factor (TF) prediction suggested that MYC may bind to ITGA9 and repress its expression and HIF-1 may bind to LAMB1 to promote its expression in MM. Additionally, pan-cancer analysis showed abnormal expression and clinical outcome associations of LAMB1 and ITGA9 in multiple cancers.
Conclusion: In conclusion, ITGA9 and LAMB1 were identified as potent biomarkers associated with cell adhesion in MM.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9